Methods and apparatus for genetic evaluation

ABSTRACT

Rapid and definitive bioagent detection and identification can be carried out without nucleic acid sequencing. Analysis of a variety of bioagents and samples, such as air, fluid, and body samples, can be carried out to provide information useful for industrial, medical, and environmental purposes. Nucleic acid samples of unknown or suspected bioagents may be collected, optimal primer pairs may be selected, and the nucleic acid may be amplified. Expected mass spectra signal models may be generated and selected, the actual mass spectra of the amplicons may be obtained. The expected mass spectra most closely correlating with the actual mass spectra may be determined using a joint maximum likelihood analysis, and base counts for the actual mass spectra and the expected mass spectra may be obtained. The most likely candidate bioagents may then be determined.

STATEMENT OF GOVERNMENT SUPPORT

This invention was made with United States Government support under DARPA/SPO contract BAA00-09. The United States Government may have certain rights in the invention.

FIELD OF THE INVENTION

Aspects of the present invention are directed generally to methods and apparatus for evaluating genetic information, and more particularly to identifying a broad range of bioagents based on their genetic information.

BACKGROUND

Rapid and definitive microbial identification is desirable for a variety of industrial, medical, environmental, quality, and research reasons. Traditionally, the microbiology laboratory has functioned to identify the etiologic agents of infectious diseases through direct examination and culture of specimens. Since the mid-1980s, researchers have repeatedly demonstrated the practical utility of molecular biology techniques, many of which form the basis of clinical diagnostic assays. Some of these techniques include nucleic acid hybridization analysis, restriction enzyme analysis, genetic sequence analysis, and separation and purification of nucleic acids (See, e.g., J. Sambrook, E. F. Fritsch, and T. Maniatis, Molecular Cloning: A Laboratory Manual, 2nd Ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., 1989). These procedures, in general, are time-consuming and tedious. Another option is the polymerase chain reaction (PCR) or other amplification procedure which amplifies a specific target DNA sequence based on the flanking primers used. Finally, detection and data analysis convert the hybridization event into an analytical result.

Other techniques for detection of bioagents include high-resolution mass spectrometry (MS), low-resolution MS, fluorescence, radioiodination, DNA chips and antibody techniques. None of these techniques is entirely satisfactory.

Mass spectrometry provides detailed information about the molecules being analyzed, including high mass accuracy. It is also a process that can be easily automated. However, high-resolution MS alone fails to perform identification of unknown or bioengineered agents, or in environments where there is a high background level of bioagents (“cluttered” background). Moreover, low-resolution MS can fail to detect some known agents, if their spectral lines are sufficiently weak or sufficiently close to those of other living organisms in the sample. DNA chips with specific probes can only determine the presence or absence of specifically anticipated organisms and fail in the presence of an unknown organism. Because there are hundreds of thousands of species of benign bacteria, some very similar in sequence to threat organisms, even arrays with 10,000 probes lack the breadth needed to differentiate one biological member from all others in such a vast population of possibilities.

Antibodies face more severe diversity limitations than arrays. If antibodies are designed against highly conserved targets to increase diversity, the false alarm problem will dominate, again because threat organisms are very similar to benign ones. Antibodies are only capable of detecting known agents in relatively uncluttered environments.

Several groups have described detection of PCR products using high resolution electrospray ionization—Fourier transform—ion cyclotron resonance mass spectrometry (ESI-FT-ICR MS). Accurate measurement of exact mass combined with knowledge of the number of at least one nucleotide allowed calculation of the total base composition for PCR duplex products of approximately 100 base pairs. (Aaserud et al., J. Am. Soc. Mass Spec. 7:1266-1269, 1996; Muddiman et al., Anal. Chem. 69:1543-1549, 1997; Wunschel et al., Anal. Chem. 70:1203-1207, 1998; Muddiman et al., Rev. Anal. Chem. 17:1-68, 1998). Electrospray ionization-Fourier transform-ion cyclotron resistance (ESI-FT-ICR) MS may be used to determine the mass of double-stranded, 500 base-pair PCR products via the average molecular mass (Hurst et al., Rapid Commun. Mass Spec. 10:377-382, 1996). The use of matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry for characterization of PCR products has been described. (Muddiman et al., Rapid Commun. Mass Spec. 13:1201-1204, 1999). However, the degradation of DNAs over about 75 nucleotides observed with MALDI limited the utility of this method.

U.S. Pat. No. 5,849,492 describes a method for retrieval of phylogenetically informative DNA sequences which comprise searching for a highly divergent segment of genomic DNA surrounded by two highly conserved segments, designing the universal primers for PCR amplification of the highly divergent region, amplifying the genomic DNA by PCR technique using universal primers, and then sequencing the gene to determine the identity of the organism.

U.S. Pat. No. 5,965,363 discloses methods for screening nucleic acids for polymorphisms by analyzing amplified target nucleic acids using mass spectrometric techniques and to procedures for improving mass resolution and mass accuracy of these methods.

WO 99/14375 describes methods, PCR primers and kits for use in analyzing preselected DNA tandem nucleotide repeat alleles by mass spectrometry.

WO 98/12355 discloses methods of determining the mass of a target nucleic acid by mass spectrometric analysis, by cleaving the target nucleic acid to reduce its length, making the target single-stranded and using MS to determine the mass of the single-stranded shortened target. Also disclosed are methods of preparing a double-stranded target nucleic acid for MS analysis comprising amplification of the target nucleic acid, binding one of the strands to a solid support, releasing the second strand and then releasing the first strand which is then analyzed by MS. Kits for target nucleic acid preparation are also provided.

PCT WO97/33000 discloses methods for detecting mutations in a target nucleic acid by nonrandomly fragmenting the target into a set of single-stranded nonrandom length fragments and determining their masses by MS.

U.S. Pat. No. 5,605,798 describes a fast and highly accurate mass spectrometer-based process for detecting the presence of a particular nucleic acid in a biological sample for diagnostic purposes.

WO 98/21066 describes processes for determining the sequence of a particular target nucleic acid by mass spectrometry. Processes for detecting a target nucleic acid present in a biological sample by PCR amplification and mass spectrometry detection are disclosed, as are methods for detecting a target nucleic acid in a sample by amplifying the target with primers that contain restriction sites and tags, extending and cleaving the amplified nucleic acid, and detecting the presence of extended product, wherein the presence of a DNA fragment of a mass different from wild-type is indicative of a mutation. Methods of sequencing a nucleic acid via mass spectrometry methods are also described.

WO 97/37041, WO 99/31278 and U.S. Pat. No. 5,547,835 describe methods of sequencing nucleic acids using mass spectrometry. U.S. Pat. Nos. 5,622,824, 5,872,003 and 5,691,141 describe methods, systems and kits for exonuclease-mediated mass spectrometric sequencing.

Thus, there is a need for a computer-assisted, computationally non-linear method for bioagent identification which is both specific and rapid, and in which no nucleic acid sequencing is required. The present invention addresses this need.

SUMMARY OF THE INVENTION

Aspects of the present invention are directed to automating the determination of a distinguishing genotypic sequence for a biological member comprising, comparing in a computationally non-linear manner a plurality of genotypic sequence regions from a plurality of biological members and determining a distinguishing genotypic sequence for said biological members, whereby said genotypic distinguishing sequence region differentiates said biological members.

A further aspect of the invention is directed to determining computationally in a non-linear manner a number of primer sets needed to provide a desired level of identification of a biological member of a biological sample comprising, determining computationally in a non-linear manner a level of identification obtained from a first primer set as applied to the biological member of said biological sample and repeating these steps with additional primer sets until a level of identification is at least equal to said desired level of identification and determining thereby the number of primer sets needed to provide the level of identification.

A still further aspect of the invention is directed to determining in a non-linear computational manner a number of primer sets needed to provide a desired level of identification of a member of a biological sample comprising obtaining a virtual amplicon of a portion of the member of the biological sample and comparing the virtual amplicon with a database of virtual amplicons, wherein the database contains virtual amplicons of correspondingly identified portions of known members of biological samples, thereby determining a level of identification of the member of the biological sample and repeating these steps with additional virtual amplicons of additional portions of said member of said biological sample until the level of identification is at or above said desired level for said member of the biological sample.

According to still further aspects of the invention, a method of determining in a non-linear computational manner a number of primer sets needed to provide a desired level of identification of a member of a biological sample comprising, obtaining a virtual amplicon of a portion of the member of the biological sample and comparing said virtual amplicon with a database of virtual amplicons, wherein said database contains virtual amplicons of corresponding identified portions of known members of biological samples, thereby determining a level of identification of said member of the biological sample, and repeating these steps with additional virtual amplicons of additional portions of the member of the biological sample until the level of identification is at or above the desired level for the member of said biological sample. In further accordance herewith, this method may be repeated providing a second virtual sample to be analyzed according to the virtual mass spectrometer having the virtual background noise, and obtaining a second real and virtual mass spectrum of the second virtual sample.

An even further aspect of the present invention is directed to a method of determining a similarity criteria of a first virtual mass spectrum of an unknown bioagent as compared to a second virtual mass spectrum of a known bioagent comprising, obtaining the mass spectrum of the first unknown bioagent corresponding to an amplicon secondary to amplification of a known segment delineated by a primer set and comparing that mass spectrum with at least the second virtual mass spectrum of the known bioagent, where the second virtual mass spectrum is of a virtual amplicon that corresponds to an identified portion of the known bioagent whereby the virtual mass spectrum is assigned a rank according to the similarity criteria with the mass spectrum of the first unknown bioagent.

A yet further aspect of the present invention is directed to a method for the generation of a synthetic mass spectrum template comprising obtaining a mass spectrum of a primer pair amplicon from a sample and transforming said mass spectra into a mass spectrum model and storing said template on computer readable medium in computer readable format.

A yet additional aspect of the present invention is directed to a method of grouping a plurality of biological members according to a grouping criteria comprising obtaining at least one grouping criteria by which each biological member is grouped and comparing the grouping criteria of at least one biological member with the grouping criteria of at least one other biological member, thereby determining an interrelatedness between said at least one biological member and said at least one other biological member and grouping said plurality of biological members according to said interrelatedness.

These and other features of the invention will be apparent upon consideration of the following detailed description of preferred embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing summary of the invention, as well as the following detailed description of preferred embodiments, is better understood when read in conjunction with the accompanying drawings, which are included by way of example, and not by way of limitation with regard to the claimed invention.

FIG. 1 is a block diagram showing a high-level overview of an illustrative embodiment of the genetic evaluation process, in accordance with at least one aspect of the present invention.

FIG. 2 is a functional block diagram of an illustrative embodiment of a primer selection method in accordance with at least one aspect of the present invention.

FIG. 3 shows an illustrative alignment of bacterial 16SrDNA sequences, showing forward and reverse primers bracketing a region to be amplified, in accordance with at least one aspect of the present invention.

FIG. 4 shows an illustrative alignment of bacterial 16SrDNA sequences, showing three primer pairs bracketing a region to be amplified in accordance with at least one aspect of the present invention.

FIG. 5 is a functional block diagram of a Maximum-likelihood processor in accordance with at least one aspect of the present invention.

FIGS. 6A and 6B show illustrative results of an embodiment of an optimum primer selection process in accordance with at least one aspect of the present invention.

FIG. 7 illustrates illustrative results of a greedy approach to primer selection over an entire alignment in accordance with at least one aspect of the present invention.

FIG. 8 shows a plot of the similarity of regions of E. coli K12 to other bacterial genomes.

FIG. 9 shows an illustrative identification of genes in E. coli strain k-12 that are highly similar to regions in other bacterial genomes, in accordance with at least one aspect of the present invention.

FIG. 10 shows an illustrative tufA protein alignment from 32 genomes in accordance with at least one aspect of the present invention.

FIG. 11 is a graph showing illustrative discrimination ranking for a master list of 16 individual primer sets, in accordance with at least one aspect of the present invention.

FIG. 12 is a graph showing illustrative discrimination ranking of combinations of primer sets from a master list of 16 primer sets.

FIG. 13 is a functional block diagram of an illustrative embodiment of a genetic evaluation system in accordance with at least one aspect of the present invention.

FIG. 14 is a graph showing illustrative data that may be obtained from a mass spectrometer in accordance with at least one aspect of the present invention.

FIG. 14-D is a graph showing an illustrative signal model that may be utilized in accordance with at least one aspect of the present invention.

FIG. 14-E is a graph showing an illustrative comparison of the mass spectrometer data of FIG. 14-C with the signal model of FIG. 14-D, in accordance with at least one aspect of the present invention.

FIG. 14-F is a graph showing an illustrative single-hypothesis cross correlation between a plurality of signal models and the mass spectrometer data of FIG. 14-C.

FIG. 14-G is a graph showing an illustrative joint hypothesis detection result using the plurality of signal models of FIG. 14-F and the mass spectrometer data of FIG. 14-C.

FIG. 15 is a tabular representation of an illustrative genomics database and the implementation of an illustrative single-primer detection scheme in accordance with at least one aspect of the present invention.

FIG. 16 is a functional block diagram of a non-linear/computer based algorithmic logic map to accomplish an illustrative maximum-likelihood identification of species scheme.

FIG. 17 is an illustrative comparison of predicted and experimental mass spectra for a mixture of bio-agents in accordance with at least one aspect of the invention.

FIG. 18 is an illustrative comparison of predicted and observed isotopic lines in a mass spectrum.

FIG. 19 is an illustrative, graphical representation of detection schemes according to one aspect of the invention.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS Overview

At a high level and referring to FIG. 1, automating the determination of a distinguishing genotypic sequence for a biological member may involve some or all of the following steps (not necessarily in this order): collecting and preparing nucleic acid samples of unknown or suspected bioagents 101; at least computer-aided determination of optimal primer pairs 102; amplifying the nucleic acid using the computer defined primer pairs to form amplicons 103; computer generating expected mass spectra signal models of a plurality of amplicons 104; obtaining the actual mass spectra of the amplicons 105; selecting a subset of the signal models 106; determining through computer evaluation and comparison, the expected mass spectra that most closely correlates with the actual mass spectra using a joint maximum likelihood analysis 107; obtaining base counts for both the actual mass spectra and the selected computer generated expected mass spectra, and matching them 108; and determining and/or ranking the most likely candidate bioagents 109.

One or more of the above may be performed in an iterative manner to further narrow the determined likely candidate bioagents. Embodiments of the present invention may further involve taking account of variations in mass spectra measurements and methods depending on type of mass spectrometer used, variations in nucleic acids for a particular bioagent or set of bioagents, and various scenarios such as expected background clutter, the source of the bioagents, and the like. Variations of this type would be expected when performing real mass spectra. Therefore, throughout this specification and appended claims, the term “real mass spectra” and “real mass spectrum” shall refer to a mass spectrum obtained from a real device and shall include all aspects normally attendant thereto. Aspects attendant thereto are well known in the art and include properties discussed above as well as others not mentioned yet known and accepted by the ordinary practitioner.

Primer design and selection methods may be used to identify oligonucleotide primer pairs that produce “amplicons” (i.e., double-stranded DNA amplification products) of nucleic acid sequences that facilitate the bioagent identification. According to one embodiment of the present invention, computer search algorithms are employed to analyze multiple alignments of numerous biological members, perhaps of different biological families. The computer algorithm may be of those known in the art but is directed to the selection of primer pairs that bind to conserved regions of the DNA that flank a variable region. According to a preferred embodiment of the present invention, a high-resolution mass spectrometer is used to determine the molecular mass of the amplicons. This molecular mass is further used to determine the base count of the amplicon. A “base count” (or “base composition”) is the number of each nucleotide base in the examined amplicon. The base counts are then input to a maximum-likelihood, or similar, detection algorithm for comparison against a database of base counts in the same amplified region. Thus, the present method combines amplification technology (which provides specificity) and a molecular mass detection mode (which provides speed and does not require nucleic acid sequencing of the amplified target sequence) for bioagent detection and identification.

Methods described herein allow extremely rapid and accurate detection and identification of bioagents compared to existing methods. Furthermore, this rapid detection and identification is possible even when sample material is impure. Thus, these methods are useful in a wide variety of fields, including, but not limited to, environmental testing (e.g., detection and discrimination of pathogenic vs. non-pathogenic bacteria in water or other samples), germ warfare (allowing immediate identification of the bioagent and appropriate treatment), pharmacogenetic analysis and medical diagnosis (including cancer diagnosis based on mutations and polymorphisms, drug resistance and susceptibility testing, screening for and/or diagnosis of genetic diseases and conditions, and diagnosis of infectious diseases and conditions). The methods take advantage of ongoing biomedical research in virulence, pathogenicity, drug resistance, and genome sequencing to provide greatly improved sensitivity, specificity, and reliability compared to existing methods.

A “bioagent” is any organism, living or dead, or a nucleic acid derived from such an organism. Examples of bioagents include, but are not limited to, cells (including human clinical samples, bacterial cells, and other pathogens) viruses, fungi, and mycoplasma. Samples may be alive or dead or in a vegetative state (for example, vegetative bacteria or spores) and may be encapsulated or bioengineered. Any bioagent can be detected and classified using methods described herein. As one example, where the bioagent is a biological threat organism, the information obtained can be used to determine practical information needed for countermeasures, including the presence in the bioagent of toxin genes, pathogenicity islands, and antibiotic resistance genes. In addition, the methods can be used to identify natural or deliberate engineering events, including chromosome fragment swapping and molecular breeding (gene shuffling). Emerging infectious disease agents can be detected and tracked.

Primer Design and Selection

Selection of primers is based on the fact that bacteria have a common set of absolutely required genes. See co-pending application serial numbers, 09/798,007 and 09/891,793. For example, about 250 genes are present in all bacterial species (Proc. Natl. Acad. Sci. U.S.A. 93, 10268, 1996; Science 270, 397, 1995), including tiny genomes such as Mycoplasma, Ureaplasma, and Rickettsia. These genes encode proteins involved in translation, replication, recombination and repair, transcription, nucleotide metabolism, amino acid metabolism, lipid metabolism, energy generation, uptake, secretion and the like. Examples of such proteins are DNA polymerase III beta, elongation factor TU, heat shock protein groEL, RNA polymerase beta, phosphoglycerate kinase, NADH dehydrogenase, DNA ligase, DNA topoisomerase, and elongation factor G. Variations in such genes can be used to detect and identify individual species of bioagents. Operons, such as the bfp operon from enteropathogenic E. coli, can also be identified. Multiple core chromosomal genes can be used to classify bioagents at a genus or species level to determine if a bioagent has threat potential. The method can also be used to detect pathogenicity markers (plasmid or chromosomal) and antibiotic resistance genes to confirm the threat potential of a bioagent and to direct countermeasures.

Although fictional, a perfect and ideal bioagent detector might identify, quantify, and report the complete nucleic acid sequence of every bioagent that reached the sensor. The complete sequence of the nucleic acid component of a pathogen would provide all relevant information about the threat, including its identity and the presence of drug-resistance or pathogenicity markers. This ideal has not yet been achieved. However, the present invention provides a straightforward strategy for obtaining information with the same practical value using base counts. While the base count of a biological fragment is not as information-rich as the entire biological sequence, where the analyte sequence fragment is properly chosen there may be no need to know the complete sequence.

A database of reference sequences can be prepared in which each sequence is indexed to a base count, so that the presence of the sequence can be inferred with accuracy from the presence of a mass spectroscopy signature corresponding to the base count. The advantage of base counts is that they can be quantitatively measured in a massively parallel fashion, for example, using multiplex PCR (PCR in which two or more primer pairs amplify target sequences simultaneously) and mass spectrometry. Cluster-specific primer pairs can be used to distinguish important local clusters (e.g., the Anthracis group).

Primers useful in methods of the invention are primers that bind to nucleotide sequence regions that flank an intervening variable region. In a preferred embodiment, the nucleotide sequence regions that flank the variable region are highly conserved among different species. For example, the nucleotide sequence regions may be highly conserved among all Bacillus or Anthracis species. Highly conserved sequences exhibit between about 80-100%, more preferably between about 90-100%, and most preferably between about 95-100% identity. The invention provides several methods for identifying such primers.

1. Procedure Using Prior Alignment of Nucleotide Sequences

FIG. 2 is a functional block diagram of one embodiment of a method for optimal selection of oligonucleotide primers for a situation in which an alignment of nucleotide sequences across species has first been constructed. In a very narrow and manual sense, such alignments can be generated for a single species, using algorithms and methods known in the art, such as Blast (Altschul et al., J. Mol. Biol. 215, 403-10, 1990), Gapped blast (Altschul et al., Nucl. Acid Res. 25, 3389-402, 1997), and Clustal W/Multiple (Thompson et al., Nucl. Acids Res. 22, 4673-80, 1994). However, where the goal is the generation of primer pairs that differentiate each species from all other species, then non-linear algorithm is required. For example, FIG. 3 shows a typical alignment of bacterial 16S ribosomal sequences constructed using the Smith-Waterman algorithm. The vertical dimension of the alignment is the species or species variant, while the horizontal dimension indicates the position of each nucleotide within the aligned region for each particular species or species variant, as shown in the exploded portion of the figure. Dashes indicate gaps inserted into particular sequences to properly align common sequences of nucleotide bases.

FIG. 3 shows possible positions of forward and reverse primers designed to amplify a region around nucleotide position 1000 in DNA sequences encoding bacterial 16S ribosomes. Detailed visual or computer inspection of the alignment can be carried out to determine whether a single primer pair will bind to and amplify all the species in the alignment or whether an additional pair or pairs of primers may be required, as shown in FIG. 4. Visual inspection is burdensome and time-consuming. Many different approaches for designing universal DNA primers have been proposed, although they are often computationally burdensome or are too limited in their primer selection criteria to be practical for the current application. See Tsunoda et al., “Time and Memory Efficient Algorithm for Extracting Palindromic and Repetitive Subsequences in Nucleic Acid Sequences,”; Evans & Wareham, “Practical Algorithms for Universal DNA Primer Design: An Exercise in Algorithm Engineering,”. To circumvent these issues, the present invention provides a series of procedures and filters that can determine an initial primer set and identify those primers that will be most useful for identification of particular bioagents. The algorithm of the present invention employs the massive strength of computer algorithms to search biological sequence space for a distinguishing genotypic sequence for each biological member.

In one embodiment, only primer sequences that already exist within an alignment are considered as candidate primers. This set of candidate primers is screened against various primer performance criteria, and only those candidates that satisfy particular rules are retained. One or more filters can be used to assess these performance criteria. Filters that can be applied include primer filters, binding filters, and pairing filters. According to methods of the present invention, Each filter can be implemented by computer. A “primer design tool” can be constructed that incorporates one or more of the filters. A block diagram of one embodiment of a primer design tool is shown in FIG. 2.

Primer Filters

A primer filter can be used to identify a primer that forms a hybrid with its nucleotide reverse complement having a melting temperature (T_(m)) of between about 20° C. and about 60° C. Preferably, the melting temperature is between about 40° C. and about 60° C. Most preferred, the melting temperature is between about 50° C. and about 60° C. The T_(m) of a hybrid can be calculated, for example, using the equation of Bolton and McCarthy, Proc. Natl. Acad. Sci. U.S.A. 48, 1390 (1962):

T _(m)=81.5° C.−16.6(log₁₀[Na⁺])+0.41(% G+C)−0.63(% formamide)−600/l), where l=the length of the hybrid in basepairs.

A primer filter also can be applied to identify primers with fewer than five GC repeats (i.e., 4, 3, 2, or 1 GC repeat) and/or primers that are between about 15 and about 25 bases or between about 18 and 23 bases in length (e.g., 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, or 27 bases).

Primers that have a GC content of between about 30% and about 80% also can be identified using a primer filter.

Primers that are too likely to self-hybridize are undesirable. One example of “self-hybridization” is the formation of a dimer between one portion of a primer and another portion of an identical copy of the primer that has a complementary nucleotide sequence. Another example of self-hybridization is the formation of a “hairpin dimer,” i.e., a dimer between one portion of a primer and another portion of the same primer that has a complementary nucleotide sequence.

A primer filter can be used to select primers that are not likely to self-hybridize. To determine the probability of self-hybridization, an AT bond is assigned a value of 2 and a GC bond is assigned a value of 4. The bond strength of a hairpin dimer preferably is less than 14. Similarly, the bond strength of a dimer formed between two identical primers is preferably less than 20. More preferably, the dimer bond strength is less than about 14.

The thresholds set by the primer filter are flexible and can be adjusted depending on how well an aligned genomic region is conserved. Preferred primers meet at least one of the five criteria described above. More preferred primers meet two, three, or four criteria. Most preferred primers meet all five criteria.

Binding Filters

The next step (step 202 of FIG. 2) in the primer selection process determines which of the remaining candidate primers will bind well to the target species. Because problems of specificity—i.e., a primer binding to more than one location within a sequence—are statistically unlikely due to the functionality of the gene, binding criteria are only applied to primer length sequences which are located at the same starting position within the alignment.

A binding filter can be applied to identify primers that hybridize with particular characteristics to at least one of the aligned nucleotide sequences. Preferably the binding filter is applied to a subset of primers which have met at least one, preferably two, three, or four, most preferably all five criteria selected for by the primer filter.

A binding filter can be set to identify, for example, primers that hybridize with a melting temperature of between about 20° C. and about 80° C. to at least one of the aligned nucleotide sequences. Melting temperature of between about 30° C. and about 70° C. are preferred with melting temperatures between about 40° C. and about 60° C. being most preferred. A binding filter also can be set to identify primers that hybridize to at least one of the aligned nucleotide sequences with no more than 2 mismatches and/or with no more than 2 mismatches in the last 4 base positions at the 3′ end of the primer (“Hamming distance”). Preferably a primer hybridizes to an aligned nucleotide sequence with no mismatches. Primers to which the binding filter is applied preferably meet two and more preferably meet all three of these criteria. Application of the binding filter is repeated for every position within the alignment in both the forward and reverse direction yielding both a forward and reverse candidate primer set.

Pairing Filter

A pairing filter (step 203 of FIG. 2) preferably is applied to a subset of primers that have met one or more of the primer filter criteria and/or binding filter criteria described above. See FIG. 2. A pairing filter combines forward and reverse primers into pairs according to the following simple rules. First, bounds on the amplicon length are imposed. An upper bound of approximately 110 bases may be required due to the limitations of the mass spectrometer. A lower bound, which is slightly more than the sum of the lengths of the forward and reverse primer pairs, is also imposed to allow for enough variable region between the two primers to promote discrimination. Thus, a pairing filter identifies pairs of primers that can produce in an amplification (e.g., PCR) reaction an amplicon of between about 50 and about 150 nucleotides in length. Preferred amplicons are between about 50 and about 100 nucleotides in length.

A minimum number of sequences covered by the pair also can be set as a selection filter. Preferred primers hybridize to at least one of the species represented by the aligned sequences. More preferred primers hybridize to at least 2, 5, 10, or 25% of the aligned sequences. Most preferred primers hybridize to at least 50% of the aligned sequences. Lastly, primer-dimerization checks are performed on the forward and reverse primers using the same self-dimerization rules described above.

Application of a Greedy Algorithm to Rank and Choose Primer Pair Sets

An ideal primer pair covers every sequence within an alignment and produces amplicons that are variable enough to uniquely determine each species in the alignment. In practice, however, single primer pairs may not bind to and amplify all species in the alignment, and multiple primers may be required to amplify a particular genomic region from all species. For example, FIG. 3 shows possible positions of forward and reverse primers designed to amplify a region around nucleotide position 1000 in the alignment of bacterial 16SrDNA sequences.

One approach is to rank each candidate primer pair identified using filters described above according to coverage and then to apply a greedy algorithm to cover as many sequences with the fewest number of primer pairs possible. Thus, the primer pair that covers the greatest number of sequences is chosen first, the primer pair that covers the greatest number of the remaining sequences is chosen second, etc. It is preferred that all primer pairs lie in the same vicinity of each other such that they will amplify the same aligned region.

In one embodiment, a greedy algorithm is used to cover vertical “stripes” (i.e., regions of sequences that are conserved among at least 2 species in the alignment) within a set of aligned nucleotide sequences, such as the alignment shown in FIG. 4. Fixing the number of primer pairs per stripe, a coverage plot such as the plot shown in FIG. 6A for bacterial 16SrDNA is created for stripes. FIG. 6A shows the percentages of the species amplified by the best five primer pairs selected by a computer employing algorithms for the filters described above for each nucleotide position of the 16S unit of the ribosome. In this example, the selected primers achieve about 50% coverage in the region from nucleotide position 56 to position 376 and approach 100% coverage in the region near position 1750.

The right-most column in FIG. 6B shows the coverage predicted by human experts without the benefit of computer algorithms for applying the filters The superior performance of the computer algorithms is demonstrated by the fact that the computer algorithms identified all the regions thought likely to yield useful primers by the human experts and, in addition, found useful primers in six regions thought “unlikely” by the human experts. An additional advantage of the computer procedure is that it identified useful primer pairs in about an hour of running time on a personal computer, whereas the selection by the human experts involved several person-months of effort.

In another embodiment, a “greedy approach” is used over the entire alignment, i.e., each consecutive primer pair is chosen anywhere within the alignment without regard to the location of the previously selected primer pairs. An example is shown in FIG. 7. Because some regions of bioagent genomes may not be well conserved, the greedy approach is less restrictive and will produce primer sets with greater coverage than the embodiment described above. For example, for the alignment shown in FIG. 7, coverage of only 50% was possible using the greedy approach over stripes, whereas the second approach was able to achieve over 70% coverage using an equal number of primer pairs.

The final step includes repeating the above procedure for other conserved loci, combining the primer pairs from these other regions using a greedy algorithm to identify primer pairs that will amplify nucleotide sequences of as many bioagents as possible with as few primer pairs as possible.

In one embodiment, primer sets can be selected using only a subset of the target sequences of interest. For example, alignments that identify primers for forty or fifty bacteria are likely to produce primer pairs that will amplify the desired regions of most of the other bacteria as well. However, this leads to the complication that all of the actual sequences are not available from which to predict mass spectroscopy signature models used in the maximum likelihood processor described below. To compensate for the lack of actual sequence data, actual mass spectroscopy measurements of amplicons from known bioagents can be used as templates. Alternatively, detection algorithms can be made robust to missing sequence data. For example, if only one amplicon of five is predictable, that amplicon is searched for and the others are treated like unknown clutter.

2. Procedure Without Prior Alignment of Sequences from Various Species

In principle, useful primer pairs can be found without first doing a multiple sequence alignment, by directly searching all possible sequence pairs and applying specificity and coverage criteria. In practice, however, this leads to extremely large computing burdens. As a useful compromise, faster, less-optimal multiple alignment procedures may be used to start the process. The alignment of a functional sequence region such as a conserved protein, for example, does not have to be perfect to support primer design. Rather, it merely has to align a region of the target sequences well enough that primer pairs can be found for that region. Simplified alignment procedures, such as nucleotide level BLAST, can be used with one or more reference bioagents (e.g., E. coli for bacteria) as a “seed” for the local alignment of other bioagent sequences in a particular gene region. This method can assist in readily identifying regions that contain genes that are largely similar across a range of bioagent genomes.

An example of the results of using a simplified alignment procedure is shown in FIG. 8. In this example, the entire genome of E. coli K-12 is locally aligned to the set of all whole genome bacterial sequences available presently through GenBank (54 genomes total, including E. coli K-12). The genomes are aligned in a pairwise manner, and any regions of similarity greater than about 80% are retained. The number of bacterial genomes that contain regions similar to E. coli are tabulated, and this number is plotted as the y-axis versus the E. coli genomic position on the x-axis.

The peaks in the similarity plot indicate specific locations where the E. coli region is similar to a locally maximum number of other bacterial regions. This inclusive set of similarity regions are collected and multiply aligned. The regions align quickly and easily due to the similarity criteria in the initial step. Once the sequences are collected, a gene that resides in the aligned region can be identified by its location on the E. coli genome. FIG. 9 shows a subset of the peaks for E. coli and their respective identifications. A representative resulting alignment, which enables the primer pair selection as previously described, is shown in FIG. 10.

Ranking Primer Choices by Discrimination Metrics

Once a master list of primers sets have been selected and primer pair sets have been ranked and chosen, additional ranking methods can be used to choose the best primers for a particular purpose. Identification methods of the invention measure the mass and, hence, identify the base counts of amplicons in a sample. Thus, optimal primer sets for use in these methods would effectively separate the base counts of all of the amplicons from different species of bioagents (e.g., different bacterial, viral, or fungal species) into unique groups. If this accomplishment were perfectly achieved, it would enable a detected base count to be classified unambiguously as belonging to a unique bioagent. In many cases, however, unambiguous separation of species by base count group is not biologically possible. It is, therefore, useful to use a “discrimination metric” to predict how well a particular primer set accomplishes the task of discriminating species. The discrimination metric ranks primer sets in an order that directly relates to the discrimination power of each of the primer sets.

To properly define the ranking criteria, the region in the four-dimensional “base count space” (A-G-C-T) occupied by all members of a particular bioagent group is first defined. This region is partially defined by collection of all of the strain sequence data for each species that would be amplified by each primer set (hypothetical PCR reactions, for example—“electronic PCR”—can be used to collect this data, as described below). Biologically likely species variants that may not be in the sequence database, however, must be taken into account. This can be accomplished, for example, using a “cloud algorithm.”

For unambiguous detection and identification of bioagents, it would be ideal if every isolate of a given species of bioagent (E. coli, for example) had exactly the same base count in any particular amplified region. However, due to naturally occurring mutations and/or deliberately engineered changes, isolates of any species might have some variation in the base count of a particular region. Because of naturally occurring variation and because engineered threat bioagents may differ slightly in particular regions from their naturally occurring counterparts, it is useful to “blur” the expected base count for a given species to allow for this variation so that the system does not miss detections. The more the expected base count is blurred, the less likely it is that a particular species will escape detection; however, such blurring will cause more overlap between the expected base counts of different species, contributing to misclassifications.

To solve this problem, expected base counts can be blurred according to the natural principles of biological mutations, customizing the specific blurring to the biological constraints of each amplified region. Each amplified region of a particular bioagent is constrained in some fashion by its biological purpose (i.e., RNA structure, protein coding, etc.). For example, protein coding regions are constrained by amino acid coding considerations, whereas a ribosome is mostly constrained by base pairing in stems and sequence constraints in unpaired loop regions. Moreover, different regions of the ribosome might have significant preferences that differ from each other.

One embodiment of application of the cloud algorithm is described in Example 1.

By collecting all likely species amplicons from a primer set and enlarging the set to include all biologically likely variant amplicons using the cloud algorithm, a suitable cluster region of base count space is defined for a particular species of bioagent. The regions of base count space in which groups of related species are clustered are referred to as “bioclusters.”

When a biocluster is constructed, every base count in the biocluster region is assigned a percentage probability that a species variant will occur at that base count. To form a probability density distribution of the species over the biocluster region, the entire biocluster probability values are normalized to one. Thus, if a particular species is present in a sample, the probability of the species biocluster integrated over all of base count space is equal to one.

At this point in the ranking procedure, proposed target species to be detected are taken into account. These generally are the bioagents that are of primary importance in a particular detection scenario. For example, if Yersinia pestis (the causative agent of bubonic and pneumonic plague) were the target, the Yersinia pestis species biocluster identified as described above, would be the “target biocluster.” To complete the example, assume that all other database species serve as the scenario background. The discrimination metric in this case is defined as the sum total of all the biocluster overlap from other species into the Yersinia pestis biocluster.

In this example, the Yersinia pestis biocluster overlap is calculated as follows. A probability of detection of 99% (P_(D)=0.99) is defined, although this value can be altered as needed. The “detection range” is defined as the set of biocluster base counts, of minimal number, that encloses 99% of the entire target biocluster. For each other bacterial species in the database, the amount of biocluster probability density that resides in the base counts in the defined detection range is calculated and is the effective biocluster overlap between that background species and the target species. The sum of the biocluster overlap over all background species serves as the metric for measuring the discrimination ability of a defined target by a proposed primer set. Mathematically, because the most discriminating primer sets will have minimal biocluster overlap, an inverse figure of merit Φ is defined,

$\Phi = {\sum\limits_{\substack{i = {all} \\ {bioclusters}}}\theta_{i}}$

where the sum is taken over the individual biocluster overlap values θ_(i) from all N background species bioclusters (i=1, . . . , N). For example, FIG. 11 shows the inverse figure of merit Φ plotted for a master list of 16 primer sets using Yersinia pestis as the target biocluster. Using the inverse figure of merit minimization criteria defined above, the result is that primer set number 4 provides the best discrimination of any of the individual primer sets in the master list.

This set of discrimination criteria also can be applied to combinations of primer sets. The respective four-dimensional base count spaces from each primer set can be dimensionally concatenated to form a (4×N)-dimensional base count space for N primer sets. Nowhere in the biocluster definition is it necessary that the biocluster reside in a four-dimensional space, thus the biocluster analysis seamlessly adapts to any arbitrary dimensionality. As a result, a master list of primer sets can be searched and ranked according to the discrimination of any combination of primer sets with any arbitrary number of primer sets making up the combination.

Using again the example of Yersinia pestis as the target, FIG. 12 shows the improved discrimination achieved through use of an increasing number of primers. For each number of primers value on the x-axis, the plotted inverse figure of merit value is that obtained from the most discriminating group (that group with the minimum figure of merit for that number of primer sets simultaneously used for discrimination). The result is that after the best groups of 3 and 4 primer sets are found, the inverse figure of merit approaches one and goes no further. That means that there is the equivalent of one background species biocluster overlapping into the target biocluster. In this example it is the Yersinia pseudotuberculosis species biocluster, which cannot be discriminated from Yersinia pestis by any combination of the 16 primer sets in the example. Thus, using the “best” 3 or 4 primer sets in the master list, Yersinia pestis is essentially discriminated from all other species bioclusters.

Generation of Amplicons

Primers selected above are used to amplify variable regions from bioagent nucleic acids in a test sample to produce double-stranded DNA amplicons. A test sample that may contain bioagents to be identified can be obtained from the air outside or inside a building, from human clinical samples (e.g., throat or nasal swabs, blood, or urine samples), food samples, swipes from clothing or furniture, or from any other source suspected of being contaminated with a biological warfare agent or a human pathogen.

The test sample is prepared for analysis by releasing nucleic acids (either DNA or RNA) from bioagents within the sample according to methods known in the art, including bead-beating and chemical lyses. However, the skilled artisan will forsee, perhaps, advantages to other methods for extracting the needed material. The preparation of the sample is outside the scope of the present claims but the skilled artisan will understand numerous way of accomplishing the desired extraction. Nucleic acid is isolated, for example, by detergent lysis of bacterial cells, centrifugation and ethanol precipitation. Nucleic acid isolation methods are described in, for example, Current Protocols in Molecular Biology (Ausubel et al.) and Molecular Cloning; A Laboratory Manual (Sambrook et al.).

Any amplification method can be used to produce the amplicons, such as polymerase chain reaction (PCR), ligase chain reaction (LCR), and strand displacement amplification (SDA). Methods of carrying out such amplification reactions are well known in the art.

PCR is preferred and can be carried out as described, for example, in Muddiman et al., Anal. Chem. 68, 3705, 1996, and Muddiman et al., Anal. Chem. 69, 1543-49, 1997.

Processing of Amplicons for Injection into a Mass Spectrometer

Amplicons from each of the amplification reactions is further processed to remove contaminants in preparation for analysis in the mass spectrum. See Muddiman et al., Anal. Chem. 68, 3705, 1996.

Generation of Base Counts from Mass Spectra of Double-Stranded DNA Amplicons

The base counts of amplicons obtained from a test sample are identified using mass measurements from mass spectrometry. Algorithms for obtaining accurate base counts of double-stranded DNA are described, for example, in Aaserud et al., Amer. Soc. Mass Spectrom. 7, 1266-69, 1996, and Muddiman et al., Anal. Chem. 69, 1543-49, 1997. Determination of monoisotopic masses and ion populations are described in Senko et al., Amer. Soc. Mass Spectrom. 6, 229-33, 1995.

Any type of mass spectrometer, such as a high resolution Fourier transform ion cylcotron resonance (FTICR) mass spectrometer or time-of-flight (TOF) mass spectrometer, can be used to obtain the mass measurements. Preferably, a mass spectrometer can provide high-precision mass measurements on the order of less than 1 ppm. See Winger et al., J. Am. Soc. Mass Spectrom. 4, 566, 1993.

A preferred FTICR mass spectrometer uses a 7 teals actively shielded super conducting magnet and modified Broker Atonics Apex II 70e ion optics and vacuum chamber. The spectrometer is interfaced to a LEAP PAL auto sampler and a custom fluidics control system for high throughput screening applications. Samples are analyzed directly from 96-well or 384-well microtiter plates at a rate of about 1 sample/minute. The Broker data-acquisition platform is supplemented with a lab-built ancillary NT data station which controls the auto sampler and contains an arbitrary waveform generator capable of generating complex rFC-excite waveforms (frequency sweeps, filtered noise, stored waveform inverse Fourier transform (SWIFT), etc.) for sophisticated tandem MS experiments. For oligonucleotides in the 20-30-mer regime typical performance characteristics include mass resolving power in excess of 100,000 (FWHM), low ppm mass measurement errors, and an operable m/z range between 50 and 5000 m/z.

A 25 watt CW CO₂ laser operating at 10.6 mm can be interfaced to the spectrometer to enable infrared multipotent dissociation (IRMPD) for oligonucleotide sequencing and other tandem MS applications. An aluminum optical bench is positioned approximately 1.5 m from the actively shielded super conducting magnet such that the laser beam is aligned with the central axis of the magnet. Using standard IR-compatible mirrors and kinematics mirror mounts, the unfocused 3 mm laser beam is aligned to traverse directly through the 3.5 mm holes in the trapping electrodes of the FTICR trapped ion cell and longitudinally traverse the hex pole region of the external ion guide finally impinging on the skimmer cone. This scheme allows IRMPD to be conducted in an m/z selective manner in the trapped ion cell (e.g., following a SWIFT isolation of the species of interest), or in a broadband mode in the high pressure region of the external ion reservoir where collisions with neutral molecules stabilize IRMPD-generated detestable fragment ions resulting in increased fragment ion yield and sequence coverage.

A TOF mass spectrometer also can be used to obtain mass spectra of amplicons. A TOF mass spectrometer measures the population of ions that arrive within a sequence of time intervals. The output digitized data from a TOF mass spectrometer differs in character from that from an FTICR mass spectrometer in that TOF data are inherently incoherent and TOF resolution is relatively coarse. The only step that is required to prepare data from a TOF mass spectrometer for maximum-likelihood processing is a time-of-flight calibration. This is obtained by measuring the arrival times for the various charge states of a known calibrant molecule.

Injection of the Sample into the Mass Spectrometer

Intact molecular ions can be generated from amplification products using one of a variety of ionization techniques to convert the sample to gas phase. These ionization methods include, but are not limited to, electro spray ionization (ESI), matrix-assisted laser resorption ionization (MALDI) and fast atom bombardment (FAB). For example, MALDI of nucleic acids, along with examples of matrices for use in MALDI of nucleic acids, are described in WO 98/54751 (Gene trace, Inc.).

The sample preferably is injected into the mass spectrometer using electro spray ionization (ESI). ESI is a gentle ionization method that produces several multiply charged ions of the parent nucleic acid without any significant fragmentation. Typically, a single charge state of the nucleic acid is isolated using a triple quadruple ion trap or ion cyclotron resonance (ICR) device. This ion is then excited and allowed to collide with a neutral gas (e.g., helium, argon, or nitrogen) to cleave certain bonds in the nucleic acid ion, or excited and fragmented with a laser pulse. Details of such techniques are well known in the art. See, e.g., U.S. Pat. Nos. 6,428,956, 5,015,845, 5,504,327, 5,504,329, 5,608,217, and 5,828,062.

Preferably, solutions to be analyzed are delivered at 150 nil/minute to a 30 mm i.d. fused-silica ESI emitter mounted on a 3-D micromanipulator. The ESI ion optics consist of a heated metal capillary, an only-only hex pole, a skimmer cone, and an auxiliary gate electrode. The 6.2 cm only-only hex pole is comprised of 1 mm diameter rods and is operated at a voltage of 380 Pv at a frequency of 5 MHz. A lab-built electro-mechanical shutter can be employed to prevent the electro spray plume from entering the inlet capillary unless triggered to the “open” position via a TTL pulse from the data station. When in the “closed” position, a stable electro spray plume is maintained between the ESI emitter and the face of the shutter. The back face of the shutter arm contains an electrometric seal which can be positioned to form a vacuum seal with the inlet capillary. When the seal is removed, a 1 mm gap between the shutter blade and the capillary inlet allows constant pressure in the external ion reservoir regardless of whether the shutter is in the open or closed position. When the shutter is triggered, a “time slice” of ions is allowed to enter the inlet capillary and is subsequently accumulated in the external ion reservoir. The rapid response time of the ion shutter (<25 ms) provides reproducible, user defined intervals during which ions can be injected into and accumulated in the external ion reservoir.

The output of the mass spectrometer is a time series of relative intensities. These data are then transformed/calibrated to allow determination of the number of molecules at each mass/charge (m/z) value. The transformed/calibrated digital mass spectrometer output is then passed to a maximum likelihood processor, which is described below. The maximum likelihood processor then makes a maximum-likelihood estimate of the number of DNA molecules of each species that were injected into the mass spectrometer. The maximum-likelihood processor ultimately carries the quantitative calibration back to a concentration estimate in the original sample.

Maximum Likelihood Processing of Amplicon Mass Spectra

Referring to FIG. 13, once PCR primer pairs have been selected as described herein, the biological entities such as bacterial or viral bioagents present in a sample may be evaluated and ultimately identified by analyzing the mass spectra of the amplicons produced by the primer pairs. As described above the mass spectra may be measured by a device such as a mass spectrometer or the like, in response to a chirp or other excitation signal applied to the amplicons. The mass spectrometer may be any type of mass spectrometer, including, but not limited to, a Fourier transform ion cylcotron resonance (FTICR) mass spectrometer or a time-of-flight (TOF) mass spectrometer.

The mass spectrometer may output mass spectra data representing some or all of the mass spectra. For example, the mass spectra data may be in the form of data representing one or more digitized time series of amplitude signals. The amplitude may represent, e.g., concentration. The mass spectra data may represent the mass spectra in any domain such as the time domain, the frequency domain, or the mass/charge (m/z) domain. The mass spectra data may be preliminarily processed in any of a number of ways (FTICR Data Prep A02). For instance, the Fourier transform may be taken of the mass spectra data (such as by fast-Fourier transform, or FFT), with appropriate weighting for side lobe control to form the coherent frequency response of the excited ions.

If the effects due to both ion-neutral collisions and non-linear interactions between charged ions are negligible, then the phase of this complex-valued response may be determined by the phase of the excitation waveform. This phase, which may be described by a second-order polynomial, may be estimated using the strongest observed spectral lines and removed from the data.

Once the excitation phase is removed, the data can be expected to be essentially real-valued and positive-definite (except perhaps for frequency sidelobe). An example of such processed mass spectra data for the amplicon produced from B. subtilis and a primer pair “23S_EC_(—)1826_(—)1924” is shown in FIG. 14-C.

The genetic evaluation system may include one or more storage devices, called herein signal model storage A04, for storing one or more signal models. An example of a signal model is shown in FIG. 14-D. The signal models may each be an estimation or other hypothesis of mass spectra for a particular hypothesized amplicon. The signal models may be in any units desired, and may have an amplitude of, e.g., concentration of units of discrete charge, and may be in the frequency or m/z domain. Some or all of the signal models may further be based on prior actual mass spectra measurements of amplicons.

The signal model storage A04 may be any storage device or collection of storage devices (such as one or more hard drives, memories, disk drives, tape drives, and the like) and may be configured as a database. The signal model storage A04 may be continuously updated as more information about mass spectra for various amplicons becomes known.

The signal model storage A04 may further associate each of the signal models with one or more of a plurality of base counts or mass distributions, and/or associate each of a plurality of base counts or mass distributions with one or more signal models. Such association may be direct or indirect, e.g., within a same database or between a plurality of cross-indexed databases. For example, a hypothesized amplicon may contain 26 adenine, 32 guanine, 24 cytosine, and 17 thymidine nucleotides. The base count for such an amplicon may be expressed herein as [26 32 24 17], or alternatively as A₂₆G₃₂C₂₄T₁₇. In the signal model storage A04, the base count(s) associated with each of the signal models may be those base counts of hypothesized amplicons that are expected to produce those signal models from a mass spectrometer. Thus, for example, base count [26 32 24 17] may be associated in the signal model storage A04 with one or more signal models that are predicted to be produced by a mass spectrometer were an amplicon with that base count measured by the mass spectrometer.

In one illustrative embodiment, the signal model storage A04 may store tens of thousands of different signal models and/or base counts, however any amount may be used. In an alternative illustrative embodiment, one or more of the signal models may be generated as needed in order to reduce storage requirements. For instance, certain parameters or other data may be stored and associated with base counts, and utilized to dynamically generate signal models from the associated base counts as required.

The more accurate the signal models, the more likely the genetic evaluation system will be able to correctly analyze the amplicons. The signals generated by a mass spectrometer are primarily determined by the mass distribution, or equivalently the base count, of the amplicons that are measured. However, estimation of the mass distribution is complicated by the fact that the number of negative charges (i.e., electrons) adhering to each DNA strand varies in a known statistical manner. These mass distributions are further complicated by the fact that the nucleotides employed in the PCR reactions are normally not monoisotopic. Rather, they contain the known natural abundances of the several isotopes of hydrogen, carbon, nitrogen, oxygen, and phosphorus. The combinations of these two distributions cause the mass spectra from a specific amplicon to appear as a sequence of spectral lines occurring at predictable discrete values of the mass-to-charge ratio. The specific form of these probability distributions, which are expected to be approximately binomial, determines the relative molecular amounts that appear at each peak. It is preferable that the predicted shapes of these envelopes also match the observations.

Some or all of the signal models retrieved from the signal model storage A04 and/or the processed mass spectra from the mass spectrometer may be calibrated (A03) to further increase the accuracy of the genetic evaluation system A00. In one illustrative embodiment, the relationship between the predicted mass-to-charge ratios and the observed frequencies of the spectral peaks in the data are determined by one or more calibration coefficients, such as two coefficients. Values for these coefficients, which may be independent of the total amplicon mass, may be estimated from the mass spectra data collected for a known low-mass calibrant molecule that is added to each sample analyzed by the mass spectrometer A01. Any calibrant molecule may be used, such a 12-mer oligonucleotide (A₁G₆C₃T₂) that has monoisotopic spectral lines in the vicinity of m/z ratios 739.122, 924.154, and 1,232.542, which may correspond to frequencies of 146,277.930, 116,988.280, and 87,714,374 Hz for a nominal seven Tesla (for example) static magnetic field that may be used in an FTICR mass spectrometer. The mass spectra for the calibrant molecule may include a plurality of calibrant lines that appear as a set of large amplitude lines. The calibrant lines may be sparsely spaced in frequency. By adjusting the calibration coefficients, the errors between the predicted and observed frequency locations of the calibrant spectral peaks may be reduced or even minimized.

The adjusted calibration coefficients may then be applied to one, some, or all of the signal models such that the frequency peaks of any measured amplicon are aligned, or at least as closely aligned as possible (such as within a threshold) with their corresponding prediction in the signal database. FIG. 14-E shows an example of the processed mass spectra data of FIG. 14-C overlaid with a calibrated version of the signal model of FIG. 14-D. As can be seen, the positions of the spectral lines of the processed mass spectra data closely align with those of the calibrated signal model.

Once the measured mass spectra and/or signal models are calibrated, they may be converted to the frequency domain (A03).

The number of molecules appearing in the measured data may be estimated (A06) for each member of a set of hypothesized bioagents. Such estimates may be made in parallel among a plurality of signal models and/or a plurality of PCR primer pairs. When maximum-likelihood processing is performed to simultaneously consider a plurality of signal models over a single primer pair, the calculations involved are referred to herein as a “joint hypothesis.” Where maximum-likelihood processing is performed simultaneously taking into account not only a plurality of signal models but also a plurality of primer pairs, such calculations are referred to herein as a “mega hypothesis.” Such mega hypotheses, which are associated with candidate bioagent strains, may be considered two-dimensional signal distributions, because they cover multiple primer pairs.

Maximum Likelihood Processing: Combine-and-Detect

Analysis of Mass Spectra to Identify Any Bacterial or Viral Organism: Approach using High Resolution FTICR Mass Spectrometer

Once optimum sets of PCR primer pairs have been selected by the procedures described above, the remaining critical task is to identify any bacterial or viral organism present in a sample by analysis of the mass spectra of the amplicons produced by the use of each primer pair to amplify a portion of the sample. The overall block diagram of the maximum-likelihood processor that optimally accomplishes this task is shown in FIG. 1. The functions performed within each block are described in the following (denoted by bold text) for the case of a FTICR mass spectrometer.

As shown in FIG. 1, the input to the maximum-likelihood processor (cf. block labeled as FTICR/TOF Mass Spec Data) is a digitized time series of the signal recorded in response to the chirp excitation applied to the ions in the cell. The first step in the processing (FTICR Data Prep) is to take the Fourier transform of the data with appropriate weighting for sidelobe control to form the coherent frequency response of the excited ions. If the effects due to both ion-neutral collisions and non-linear interactions between charged ions are negligible, then the phase of this complex-valued response is determined by the phase of the excitation chirp waveform. This phase, which is described by a second-order polynomial, is estimated using the strongest observed spectral lines and removed from the data. Once the excitation phase is removed, the data should be essentially real-valued and positive-definite (except for frequency sidelobes).

The maximum-likelihood processor operates by comparing hypothesized mass spectra for the strands of DNA expected for each species amplified by each pair of PCR primers. In order for this procedure to be successful, it is important to have a database (Signal Data Base) of pre-computed signal predictions that accurately match the measurements. The signals for a mass spectrometer are primarily determined by their mass or equivalently their base count. The later quantity is determined by the total number of each of the four nucleotides in the amplicon; i.e., the number of adenine, guanine, cytosine and thymine bases. The expected mass distributions, however, are complicated by the fact that the number of negative charges (electrons) adhering to each DNA strand varies in a known statistical manner. In addition, these distributions are complicated by the fact that the nucleotides employed in the PCR reactions are normally not monoisotopic. Rather, they contain the known natural abundances of the several isotopes of hydrogen, carbon, nitrogen, oxygen, and phosphorus. The combinations of these two distributions cause the signal from a specific amplicon to appear as a sequence of spectral lines occurring at predictable discrete values of the mass-to-charge ratio. The specific form of these probability distributions, which are expected to be approximately binomial, determines the relative molecular amounts that appear at each peak. It is important that the predicted shape of these envelopes also match the observations.

The next step (Signal Calibration) required to prepare the signal predictions for the maximum likelihood processor is frequency calibration. The relationship between the predicted mass-to-charge ratios and the observed frequencies of the spectral peaks in the data are determined by two calibration coefficients. Values for these coefficients, which are independent of the total amplicon mass, are estimated from the data collected for a known low-mass calibrant molecule that is added to each sample sprayed into the mass spectrometer. The calibrant lines appear as a set of large amplitude lines that are sparsely spaced in frequency. By adjusting the calibration coefficients, the errors between the predicted and observed frequency locations of the calibrant spectral peaks are minimized. The resulting coefficients are then applied to the entire database of pre-calculated signatures so that the frequency peaks of any measured amplicon are aligned with their corresponding prediction in the signal database.

The maximum-likelihood processor estimates the molecular amount appearing in the measured data for each member of set of hypothesized organisms. These ‘mega-hypotheses’, which are associated with candidate organism strains, are two-dimensional signal distributions since they cover multiple primer sets. The processor forms these hypotheses (Predict Signal Hypotheses) by extracting from the signal database the corresponding frequency distribution for the associated amplicon base count at each primer pair.

The base count information needed for each organism is obtained from a genomics database. That database (Genomics Data Base) is formed from either observations or predictions of PCR results on all known bacterial strains or viruses for each primer pair. In general, this information includes the base counts for each operon and the number of copies that appear within the genome. It may also be known that a particular strain fails to prime for a particular primer pair. In that case, there would be no signal expected for that primer pair. In addition, to detect new strain variations or virus mutations small shifts from the expected base counts are also added to the list of hypothesized organisms. The allowed shifts are determined from data tables that quantify the probability of them occurring for each primer pair.

In order to form the organism hypotheses over multiple primer pairs, it is also necessary to account for variations in PCR gains that may occur. That is, the number of DNA dimers obtained from a common organism sample may differ between primer pairs. This information may be obtained from a database of PCR gains (PCR Data Base). Real-time, adaptive gain calibration, can also be enhanced by inclusion of PCR calibrants in every PCR reaction, which not only provide gains, but provide a quality control function to identify failed reactions. Furthermore, in general, the amplicons from the forward and reverse strands do not always occur in equal amounts and additional single-strand PCR by-products can occur. The later includes both non-blunt end products (e.g., additional adenines attached to some fraction of the strands) and partially digested amplicons (missing bases at the 3′ end to some fraction of the strands). This information, which depends on the primer pair and the polymerase selected for PCR, is also needed to accurately predict the signatures observed in the mass spectrometer. This should also appear in this database.

The final piece of information needed to implement the maximum likelihood processor is an estimate of the background noise (Noise Estimate). This includes the effects of both electronic noise (expected to be a zero-mean Gaussian process) and chemical ion noise (associated with Poisson fluctuations). In general, both noise components vary with frequency. The chemical noise, which is characterized by a non-zero mean and variance, appears as a sequence of low-amplitude frequency peaks. This noise may be estimated from data sets that do not contain genomic material.

The molecular amounts for the hypothesized organisms are obtained by determining the scale factors that produce the ‘best’ statistical fit of the mega-hypotheses to the data (Max Likelihood Estimates). An iterative algorithm, which maximizes the likelihood that the measurements are consistent with the signal statistics, is used to calculate these amounts. This algorithm, which bears a strong resemblance to a least-squares algorithm, minimizes the whitened residual between the measured data and the estimated signals. The whitener normalizes the calculated residual power at each frequency bin by the expected noise variance. This includes effects of electronic noise, chemical noise and also signal noise (associated with Poisson sampling fluctuations). The molecular amounts are estimated jointly in order to account for any correlations that occur between different organism hypotheses. In addition, the estimated amounts are also constrained to be non-negative as is required for them to be physically sensible.

The next block of the processor (Detect Pathogens) determines if any member of a list (may depend on type of collection) of biological pathogens is present. A Generalized Likelihood Ratio Test (GLRT) is used to make that decision. This test replaces, in the likelihood ratio, the unknown organism amounts by their maximum likelihood estimates. This includes estimates for both the pathogen and all additional background organisms. The GLRT decides that a pathogen is present if the likelihood ratio (defined for the individual pathogen relative to the background) exceeds a selected threshold. A separate test is performed for each pathogen in the list. The actual value of the threshold depends on both the desired false alarm rate and the background characteristics. Finally, the detected hypotheses may not uniquely identify an organism. For example, it may be possible to associate a detected hypothesis with strain variations from multiple species. In such a case, posterior probabilities, which are determined from the biological probability tables in the genomics database, are calculated for each of the ambiguous organisms. These indicate the probability that each candidate species is consistent with the achieved detection.

The detection capabilities of this processor can be improved by exploiting a priori information (A Priori Information) about the expected clutter and pathogens. That is, the expected background organisms and pathogens depend on the nature of the collected samples. As an example, for clinical applications these can depend on the type of sample (i.e., blood, urine, etc.), patient group, time of year and geographical location. Information about background organisms can also obtained by monitoring the results acquired from common locations and times. This data, which is quantified as a table of priori probabilities for each organism, can be used in the processor in variety of ways. In particular, a priori probabilities can be included in the calculation of the posterior probabilities to improve the association of detections with species. Furthermore, a priori information can be used to minimize the number of hypotheses since there is no need to test signals that have zero probability of appearing in the analyzed sample.

The final processing block (Test Unknowns) determines if any unrecognized species are present in the collected sample. This is achieved by examining the residuals, which are obtained by subtracting the identified signals from the measurements, to determine if they are above the system noise floor. In such a case, the residual data can be examined to determine if its characteristics are consistent with signals associated with non-hypothesized base counts. The primary tool for this analysis is a mass deconvolution algorithm, which identifies additional, unhypothesized masses in the spectrum and then associates their mass to a set of possible base counts based on mass resolution of the spectrometer. These residual, additional basecounts at the single primer step, can then be analyzed with output of the other primers and mapped to a phylogenetic tree for possible identification. If it is decided that additional unknown organisms may be present then additional tests can be requested. Once the characteristics of a new signal are verified, then it would be added to the signal database for all subsequent tests.

It may further be determined whether any unrecognized species are present in the collected sample. This may be achieved by examining the residual, which may be obtained by subtracting the identified signals from the measurements, to determine if the residual is above the system noise floor. Where the residual is above the noise floor, the residual may be examined to determine if its characteristics are consistent with signals associated with non-hypothesized base counts. If it is decided that additional unknown bioagents may be present in the residual then additional tests may be requested. Once the characteristics of a new signal are verified, then it may be added to the signal database A04 for subsequent tests.

While illustrative systems and methods as described herein embodying various aspects of the present invention are shown by way of example, it will be understood, of course, that the invention is not limited to these embodiments. Modifications may be made by those skilled in the art, particularly in light of the foregoing teachings. For example, each of the elements of the aforementioned embodiments may be utilized alone or in combination with elements of the other embodiments. Although the invention has been defined using the appended claims, these claims are exemplary in that the invention is intended to include the elements and steps described herein in any combination or sub combination. Accordingly, there are any number of alternative combinations for defining the invention, which incorporate one or more elements from the specification, including the description, claims, and drawings, in various combinations or sub combinations. It will be apparent to those skilled in the relevant technologies, in light of the present specification, that alternate combinations of aspects of the invention, either alone or in combination with one or more elements or steps defined herein, may be utilized as modifications or alterations of the invention or as part of the invention. It is intended that the written description of the invention contained herein covers all such modifications and alterations.

All patents, patent applications, and references cited in this disclosure are incorporated by reference herein in their entirety.

Example 1 Modifications to Account for Biologically Likely Species Variants (“Cloud Algorithm”)

Base count blurring can be carried out as follows. “Electronic PCR” can be conducted on nucleotide sequences of the desired bioagents to obtain the different expected base counts that could be obtained for each primer pair. In one illustrative embodiment, one or more spreadsheets, such as Microsoft Excel workbooks contains a plurality of worksheets. First in this example, there is a worksheet with a name similar to the workbook name; this worksheet contains the raw electronic PCR data. Second, there is a worksheet named “filtered bioagents base count” that contains bioagent name and base count; there is a separate record for each strain after removing sequences that are not identified with a genus and species and removing all sequences for bioagents with less than 10 strains. Third, there is a worksheet, “Sheet1” that contains the frequency of substitutions, insertions, or deletions for this primer pair. This data is generated by first creating a pivot table from the data in the “filtered bioagents base count” worksheet and then executing an Excel VBA macro. The macro creates a table of differences in base counts for bioagents of the same species, but different strains. One of ordinary skill in the art may understand additional pathways for obtaining similar table differences without undo experimentation.

Application of an exemplary script, involves the user defining a threshold that specifies the fraction of the strains that are represented by the reference set of base counts for each bioagent. The reference set of base counts for each bioagent may contain as many different base counts as are needed to meet or exceed the threshold. The set of reference base counts is defined by taking the most abundant strain's base type composition and adding it to the reference set and then the next most abundant strain's base type composition is added until the threshold is met or exceeded. The current set of data was obtained using a threshold of 55%, which was obtained empirically.

For each base count not included in the reference base count set for that bioagent, the script then proceeds to determine the manner in which the current base count differs from each of the base counts in the reference set. This difference may be represented as a combination of substitutions, Si=Xi, and insertions, Ii=Yi, or deletions, Di=Zi. If there is more than one reference base count, then the reported difference is chosen using rules that aim to minimize the number of changes and, in instances with the same number of changes, minimize the number of insertions or deletions. Therefore, the primary rule is to identify the difference with the minimum sum (Xi+Yi) or (Xi+Zi), e.g., one insertion rather than two substitutions. If there are two or more differences with the minimum sum, then the one that will be reported is the one that contains the most substitutions.

Differences between a base count and a reference composition are categorized as either one, two, or more substitutions, one, two, or more insertions, one, two, or more deletions, and combinations of substitutions and insertions or deletions. Tables 1-8 illustrate these changes. The number of possible changes within each category is termed the complexity and is shown in Table 9.

The workbook contains a worksheet for each primer pair; the tables in each worksheet summarize the frequency of the types of base count changes. One worksheet can show the mean and standard deviation for each base count change type over the ten primer pairs.

The results of the above described procedure are presented in tables 1 through 10.

TABLE 1 Single Substitutions A→C transversion A→G transition A→T transversion C→A transversion C→G transversion C→T transition G→A transition G→C transversion G→T transversion T→A transversion T→C transition T→G transversion

TABLE 2 Two Substitutions A A→CC 2 transversions A A→CG transition and transversion A A→CT 2 transversions A G→CC 2 transversions A G→CT 2 transversions A T→CC transition and transversion A A→GG 2 transitions A A→GT transition and transversion A C→GG transition and transversion A C→GT 2 transitions A T→GC 2 transitions A T→GG transition and transversion A A→TT 2 transversions A C→TT transition and transversion A G→TT 2 transversions C C→AA 2 transversions C C→AG 2 transversions C C→AT transition and transversion C G→AA transition and transversion C G→AT 2 transitions C T→AA 2 transversions C T→AG 2 transversions C C→GG 2 transversions C C→GT transition and transversion C T→GG 2 transversions C C→TT 2 transitions C G→TT transition and transversion G G→AA 2 transitions G G→AC transition and transversion G G→AT transition and transversion G T→AA transition and transversion G T→AC 2 transitions G G→CC 2 transversions G G→CT 2 transversions G T→CC transition and transversion G G→TT 2 transversions T T→AA 2 transversions T T→AC transition and transversion T T→AG 2 transversions T T→CC 2 transitions T T→CG transition and transversion T T→GG 2 transversions

TABLE 3 Single Insertion →A →C →G →T

TABLE 5 Single Deletion A→ C→ G→

TABLE 4 Two Insertions →AA →AC →AG →AT →CC →CG →CT →GG →GT →TT

TABLE 6 Two Deletions AA→ AC→ AG→ AT→ CC→ CG→ CT→ GG→ GT→

TABLE 7 One Substitution and One Insertion A→CC A→CG A→CT A→GG A→GT A→TT C→AA C→AG C→AT C→GG C→GT C→TT G→AA G→AC G→AT G→CC G→CT G→TT T→AA T→AC T→AG T→CC T→CG T→GG

TABLE 8 One Substitution and One Deletion AA→C AA→G AA→T AC→G AC→T AG→C AG→T AT→C AT→G CC→A CC→G CC→T CG→A CG→T CT→A CT→G GG→A GG→C GG→T GT→A GT→C TT→A TT→C TT→G

TABLE 9 Complexity of base count changes Type of base composition change Comp Single Substitution Purine→Purine Purine→Pyrimidine Pyrimidine→Purine Pyrimidine→Pyrimidine Single Transition Single Transversion Two Substitutions Two Transitions One Transition & One Transversion Two Transversions Three Substitutions Single Purine One Insertion Single Pyrimidine Two Insertions Two Purines One Purine & One Pyrimidine Two Pyrimidines Three Insertions Single Purine One Deletion Single Pyrimidine Two Deletions Two Purines One Purine & One Pyrimidine Two Pyrimidines Three Deletions Purine→TwoPurines One Insertion & One Purine→One Purine & One Pyrimidine Substitution Purine→TwoPyrimidines Pyrimidine→TwoPurines Pyrimidine→One Purine & One Pyrimidine Pyrimidine→TwoPyrimidines Single Transition & One Purine Insertion Single Transition & One Pyrimidine Insertion Single Transversion & One Purine Insertion Single Transversion & One Pyrimidine Insertion One Deletion & One Two Purines→Purine Substitution One Purine & One Pyrimidine→Purine Two Pyrimidines→Purine Two Purines→Pyrimidine One Purine & One Pyrimidine→Pyrimidine Two Pyrimidines→Pyrimidine Single Transition & One Purine Deletion Single Transition & One Pyrimidine Deletion Single Transversion & One Purine Deletion Single Transversion & One Pyrimidine Deletion

TABLE 10 Average Frequencies of Various Base Composition Changes Deduced from Electronic PCR of 16S Ribosomal Data Base Strains/ Base Compositions/ Strains Complexity Compositions Complexity Strain Threshold = 55% Average Std. Dev. Average Std. Dev. Average Std. Dev. Average Std. Dev. No Changes 85.9% 5.7% 85.9% 5.7% 41.8% 7.6% 41.8%  7.6% All Changes 14.1% 5.7% 58.2% 7.6% Single Substitution 7.5% 3.1% 0.63% 0.3% 29.5% 2.5% 2.5% 0.21% Purine -> Purine 2.6% 1.6% 1.29% 0.8% 8.5% 2.5% 4.3% 1.23% Purine -> Pyrimidine 1.0% 0.5% 0.24% 0.1% 5.4% 2.3% 1.4% 0.58% Pyrimidine -> Purine 1.1% 0.4% 0.28% 0.1% 5.8% 2.0% 1.5% 0.50% Pyrimidine -> Pyrimidine 2.9% 1.2% 1.44% 0.6% 9.7% 2.1% 4.9% 1.03% Single Transition 5.5% 2.5% 1.36% 0.6% 18.2% 2.5% 4.6% 0.63% Single Transversion 2.1% 0.7% 0.26% 0.1% 11.2% 2.2% 1.4% 0.27% Two Substitutions 2.5% 1.2% 0.06% 0.0% 9.7% 2.9% 0.2% 0.07% Two Transitions 1.2% 0.9% 0.17% 0.1% 3.7% 1.1% 0.5% 0.16% One Transition & One Transversion 0.6% 0.4% 0.04% 0.0% 2.8% 1.7% 0.2% 0.11% Two Transversions 0.7% 0.6% 0.04% 0.0% 3.2% 1.7% 0.2% 0.09% Three or More Substitutions 1.0% 1.0% 0.01% 0.0% 4.5% 3.2% 0.0% 0.03% One Insertion 1.0% 1.0% 0.26% 0.2% 3.8% 2.5% 0.9% 0.62% Single Purine 0.6% 0.5% 0.28% 0.2% 2.1% 1.1% 1.1% 0.57% Single Pyrimidine 0.5% 0.8% 0.24% 0.4% 1.6% 1.5% 0.8% 0.77% Two Insertions 0.1% 0.2% 0.01% 0.0% 0.5% 0.6% 0.1% 0.06% Two Purines 0.0% 0.0% 0.01% 0.0% 0.2% 0.3% 0.1% 0.08% One Purine & One Pyrimidine 0.1% 0.1% 0.02% 0.0% 0.2% 0.3% 0.1% 0.08% Two Pyrimidines 0.0% 0.0% 0.01% 0.0% 0.1% 0.2% 0.0% 0.06% Three or More Insertions 0.1% 0.1% 0.00% 0.0% 0.5% 0.5% 0.0% 0.03% One Deletion 0.6% 0.4% 0.15% 0.1% 3.2% 1.8% 0.8% 0.44% Single Purine 0.3% 0.2% 0.17% 0.1% 1.7% 0.9% 0.9% 0.43% Single Pyrimidine 0.3% 0.3% 0.13% 0.1% 1.5% 1.3% 0.7% 0.66% Two Deletions 0.1% 0.2% 0.01% 0.0% 0.9% 1.0% 0.1% 0.10% Two Purines 0.0% 0.1% 0.02% 0.0% 0.4% 0.5% 0.1% 0.15% One Purine & One Pyrimidine 0.1% 0.1% 0.02% 0.0% 0.3% 0.6% 0.1% 0.14% Two Pyrimidines 0.0% 0.0% 0.01% 0.0% 0.2% 0.3% 0.1% 0.08% Three or More Deletions 0.1% 0.1% 0.00% 0.0% 0.4% 0.4% 0.0% 0.02% One Insertion & One Substitution 0.1% 0.1% 0.00% 0.0% 0.7% 0.5% 0.0% 0.02% Purine -> Two Purines 0.0% 0.0% 0.00% 0.0% 0.0% 0.0% 0.0% 0.00% Purine -> One Purine & One Pyrimidine 0.0% 0.0% 0.00% 0.0% 0.1% 0.2% 0.0% 0.05% Purine -> Two Pyrimidines 0.0% 0.0% 0.00% 0.0% 0.2% 0.2% 0.0% 0.03% Pyrimidine -> Two Purines 0.0% 0.0% 0.00% 0.0% 0.2% 0.3% 0.0% 0.04% Pyrimidine -> One Purine & One Pyrimidine 0.0% 0.0% 0.01% 0.0% 0.2% 0.3% 0.0% 0.07% Pyrimidine -> Two Pyrimidines 0.0% 0.0% 0.00% 0.0% 0.0% 0.0% 0.0% 0.00% One Deletion & One Substitution 0.2% 0.2% 0.01% 0.0% 1.1% 0.9% 0.0% 0.04% Two Purines -> Purine 0.0% 0.0% 0.00% 0.0% 0.0% 0.0% 0.0% 0.00% One Purine & One Pyrimidine -> Purine 0.0% 0.0% 0.01% 0.0% 0.4% 0.4% 0.1% 0.11% Two Pyrimidines -> Purine 0.0% 0.1% 0.01% 0.0% 0.1% 0.2% 0.0% 0.04% Two Purines -> Pyrimidine 0.0% 0.0% 0.00% 0.0% 0.2% 0.3% 0.0% 0.05% One Purine & One Pyrimidine -> Pyrimidine 0.0% 0.1% 0.01% 0.0% 0.2% 0.3% 0.1% 0.08% Two Pyrimidines -> Pyrimidine 0.0% 0.0% 0.01% 0.0% 0.1% 0.3% 0.1% 0.13% >=1 Insertions/Deletions & >=1 Substitutions 0.8% 1.3% 3.5% 3.7% 

1. A method of automating the determination of a distinguishing genotypic sequence for a biological member comprising: (a) comparing in a computationally non-linear manner a plurality of genotypic sequence regions from a plurality of biological members; and (b) determining a distinguishing genotypic sequence for said biological members, whereby said genotypic distinguishing sequence region differentiates said biological members.
 2. The method according to claim 1 wherein the plurality of biological members are members selected from a family and said distinguishing genotypic sequence differentiates genus of said family.
 3. The method according to claim 1 wherein the computationally non-linear manner is a gene-space search algorithm.
 4. The method according to claim 3 wherein the gene-space search algorithm is a computer executable instruction set whereby said plurality of genotypic sequence regions are searched in a non-linear manner.
 5. The method according to claim 1 further comprising: determining computationally at least one additional distinguishing genotypic sequence for said biological members, whereby said at least one additional genotypic distinguishing sequence region, is synergistically distinguishing with said distinguishing genotypic sequence to further differentiate said biological members.
 6. The method according to claim 2 wherein the plurality of biological members are members selected from a genus and said distinguishing genotypic sequence differentiates species of said genus.
 7. The method according to claim 2 wherein the plurality of biological members are members selected from a species and said distinguishing genotypic sequence differentiates sub-species of said species.
 8. The method according to claim 1 wherein the computationally non-linear manner is a non-sequential gene-space search algorithm.
 9. The method according to clam 1 wherein the computationally nonlinear manner is a parallel gene-space search algorithm.
 10. A method of determining computationally in a non-linear manner a number of primer sets needed to provide a desired level of identification of a biological member of a biological sample comprising: (a) determining computationally in a non-linear manner a level of identification obtained from a first primer set as applied to said biological member of said biological sample, and; (b) repeating step (a) with additional primer sets until said level of identification is at least equal to said desired level of identification and determining thereby said number of primer sets needed to provide said level of identification.
 11. The method according to claim 10 wherein said non-linear manner is a gene-space search algorithm.
 12. The method according to claim 11 wherein said gene-space search algorithm is a non-sequential search algorithm.
 13. The method according to claim 10 wherein said level of identification is a likelihood of differentiation of said biological member.
 14. The method according to claim 10 wherein said number of primer sets is a number of primer pair combinations wherein each primer pair combination synergistically augments said level of identification of said biological member greater than 1-fold.
 15. The method according to claim 12 wherein the gene-space search algorithm is a computer executable instruction set whereby a plurality of primer sets are searched in parallel.
 16. The method according to claim 12 wherein the gene-space search algorithm is a computer executable instruction set whereby a plurality of primer sets are searched simultaneously.
 17. The method according to claim 13 wherein said likelihood of differentiation of said biological member is a statistical likelihood of differentiation.
 18. A method of determining in a non-linear computational manner a number of primer sets needed to provide a desired level of identification of a member of a biological sample comprising: (a) obtaining a virtual amplicon of a portion of said member of said biological sample; (b) comparing said virtual amplicon with a database of virtual amplicons, wherein said database contains virtual amplicons of corresponding identified portions of known members of biological samples, thereby determining a level of identification of said member of said biological sample; (c) repeating step (b) with additional virtual amplicons of additional portions of said member of said biological sample until said level of identification is at or above said desired level for said member of said biological sample.
 19. The method according to claim 18 wherein said non-linear manner is a gene-space search algorithm.
 20. The method according to claim 19 wherein said gene-space search algorithm is a non-sequential search algorithm. 